Hi, I am pretty new to Mplus and to structural equation modeling (SEM) in general. I have a dataset with cross sectional-time series data structure: a list of variables for 170 countries measured across 20 years.

These variables include: dependent variable, which is a count variable; and variables which are supposed to be indicators of 4 latent variables; and finally a set of control variables.

I want to estimate both the independent and interaction effects of the 4 latent variables on the count variable, controlling for the control variables. I wonder whether this type of analysis could be done using structural equation modeling, which type of SEM I should use, and whether MPLUS can achieve this.

It sounds like you want to regress a count variable on a set of observed and latent exogenous variables and their interactions. That can be done in Mplus. Each interaction between two latent variables or a latent variable and an observed variable requires one dimension of integration. A model with more than four dimensions of integration is not recommended.

The interactions in my estimation will be mostly two-way interactions among the 4 latent variables, which means theoretically there can be as many as 6 two-way interactions. Can MPLUS accommodate this many interactions?

Also panel data regression can be estimated by fix-effects and random effects models. If I want to estimate three models: country-level fixed effect model, country and year two-way fixed effects model, and random effect model; can MPLUS do all three? And can you refer me to some example syntax?

And both my independent variables and dependent variables are measured on 20 time points. I understand for panel data with only a few waves, where for example, independent variable is measured in wave 1 and dependent variables measured in wave 2, SEM can be easily estimated.

But for my case, both independent variables and dependent variables are measured in 20 waves, where I hope to use independent variables measured in time t-1 to estimate dependent variable in time t, how shall I specify the model in MPLUS? If there is no latent variables, I can simply use xtpoisson in stata to do the job, the existence of latent variables just complicates the estimation. Hope MPLUS is more powerful on this type of analysis.

A model with more than four dimensions of integration is not recommended. This would be four latent variable interactions. It is not likely that all are signficant so I would investigate that and include on the ones that are significant.

I have run across a very interesting paper that purports to model time series cross sectional data as a multi-level SEM. What makes this stand out is that the data for each time period is cross sectional. That is, a unique sample of respondents is collected for each time period without any overlap of Rs. The link is below.